'''
 * Tag2Text
 * Written by Xinyu Huang
'''
import warnings
warnings.filterwarnings("ignore")

from .vit import VisionTransformer, interpolate_pos_embed
from .swin_transformer import SwinTransformer, interpolate_relative_pos_embed
from .med import BertConfig, BertModel, BertLMHeadModel
from .utils import tra_array
from transformers import BertTokenizer

import torch
from torch import nn
import torch.nn.functional as F

import os
from urllib.parse import urlparse
from timm.models.hub import download_cached_file
import json
import math
import numpy as np

def read_json(rpath):
    with open(rpath, 'r') as f:
        return json.load(f)

# delete some tags that may disturb captioning
# 127: "quarter"; 2961: "back"; 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one"
delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359]

# adjust thresholds for some tags
# default threshold: 0.68
# 2701: "person"; 2828: "man"; 1167: "woman"; 
tag_thrshold = {2701:0.7, 2828: 0.7, 1167: 0.7}
        
class Tag2Text_Caption(nn.Module):
    def __init__(self,                 
                 med_config = 'configs/med_config.json',  
                 image_size = 384,
                 vit = 'base',
                 vit_grad_ckpt = False,
                 vit_ckpt_layer = 0,
                 prompt = 'a picture of ',
                 threshold = 0.68,
                 ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """            
        super().__init__()

        if vit=='swin_b':
            if image_size == 224:
                vision_config_path = 'configs/swin/config_swinB_224.json'
            elif image_size == 384:
                vision_config_path = 'configs/swin/config_swinB_384.json'
            vision_config = read_json(vision_config_path)
            assert image_size == vision_config['image_res']
            # assert config['patch_size'] == 32
            vision_width = vision_config['vision_width']

            self.visual_encoder = SwinTransformer(img_size=vision_config['image_res'],
                                            patch_size=4,
                                            in_chans=3,
                                            embed_dim=vision_config['embed_dim'],
                                            depths=vision_config['depths'],
                                            num_heads=vision_config['num_heads'],
                                            window_size=vision_config['window_size'],
                                            mlp_ratio=4.,
                                            qkv_bias=True,
                                            drop_rate=0.0,
                                            drop_path_rate=0.1,
                                            ape=False,
                                            patch_norm=True,
                                            use_checkpoint=False)
        
        else:
            self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)


        self.tokenizer = init_tokenizer()   

        # create the decoder
        decoder_config = BertConfig.from_json_file(med_config)
        decoder_config.encoder_width = 768
        self.text_decoder = BertLMHeadModel(config=decoder_config)     

        # create encoder
        encoder_config = BertConfig.from_json_file(med_config)
        encoder_config.encoder_width = vision_width
        self.tag_encoder = BertModel(config=encoder_config, add_pooling_layer=False)
        
        self.prompt = prompt
        self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1

        self.threshold = threshold
        num_features = 768
        self.num_class = 3429

        q2l_config = BertConfig.from_json_file('configs/q2l_config.json')
        q2l_config.encoder_width = vision_width
        self.vision_multi = BertModel(config=q2l_config, add_pooling_layer=False)
        self.vision_multi.resize_token_embeddings(len(self.tokenizer)) 
        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
        self.fc =  GroupWiseLinear(self.num_class, num_features, bias=True)
        self.del_selfattention()

        tie_encoder_decoder_weights(self.tag_encoder,self.vision_multi,'',' ')
        self.tag_array = tra_array

        self.class_threshold = torch.ones(self.num_class) * self.threshold
        for key,value in tag_thrshold.items():
            self.class_threshold[key] = value
    
    def del_selfattention(self):
        del self.vision_multi.embeddings
        for layer in self.vision_multi.encoder.layer:
            del layer.attention
        
    def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0, tag_input = None, return_tag_predict = False):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)

        #==============generate tag==============#
        if tag_input == None:
            image_spatial_embeds = image_embeds[:,1:,:]
            image_cls_embeds = image_embeds[:,0,:]

            bs = image_spatial_embeds.shape[0]
            label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs,1,1)
            mlr_tagembedding = self.vision_multi(encoder_embeds = label_embed,
                                encoder_hidden_states = image_embeds,
                                encoder_attention_mask = image_atts,      
                                return_dict = False,
                                mode = 'mlr',
                                )  

            logits = self.fc(mlr_tagembedding[0])
            
            # targets = torch.where(torch.sigmoid(logits) > self.threshold , torch.tensor(1.0).to(image.device), torch.zeros(self.num_class).to(image.device))
            targets = torch.where(torch.sigmoid(logits) > self.class_threshold.to(image.device) , torch.tensor(1.0).to(image.device), torch.zeros(self.num_class).to(image.device))

            tag = targets.cpu().numpy()
            tag[:,delete_tag_index] = 0
            bs = image.size(0)
            tag_input = []
            for b in range(bs):
                index = np.argwhere(tag[b] == 1)
                token = self.tag_array[index].squeeze(axis = 1)
                tag_input.append(' | '.join(token))            
        #========================================#
        
        if not sample:
            image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
            image_atts = image_atts.repeat_interleave(num_beams,dim=0)
            tag_input_temp = []
            for tag in tag_input:
                for i in range(num_beams):
                    tag_input_temp.append(tag)
            tag_input = tag_input_temp


        tag_input_tokenzier = self.tokenizer(tag_input, padding='max_length', truncation=True, max_length=40, 
                              return_tensors="pt").to(image.device)  
        encoder_input_ids = tag_input_tokenzier.input_ids
        encoder_input_ids[:,0] = self.tokenizer.enc_token_id

        output_tagembedding = self.tag_encoder(encoder_input_ids,
                                       attention_mask = tag_input_tokenzier.attention_mask,
                                       encoder_hidden_states = image_embeds,
                                       encoder_attention_mask = image_atts,      
                                       return_dict = True,
                                      )  
        
        prompt = [self.prompt] * image.size(0)
        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) 
        input_ids[:,0] = self.tokenizer.bos_token_id
        input_ids = input_ids[:, :-1] 

        if sample:
            #nucleus sampling
            model_kwargs = {"encoder_hidden_states": output_tagembedding.last_hidden_state, "encoder_attention_mask":None}
            outputs = self.text_decoder.generate(input_ids=input_ids,
                                                max_length=max_length,
                                                min_length=min_length,
                                                do_sample=True,
                                                top_p=top_p,
                                                num_return_sequences=1,
                                                eos_token_id=self.tokenizer.sep_token_id,
                                                pad_token_id=self.tokenizer.pad_token_id, 
                                                repetition_penalty=1.1,                                            
                                                **model_kwargs)
        else:
            #beam search
            model_kwargs = {"encoder_hidden_states": output_tagembedding.last_hidden_state, "encoder_attention_mask":None}
            outputs = self.text_decoder.generate(input_ids=input_ids,
                                                max_length=max_length,
                                                min_length=min_length,
                                                num_beams=num_beams,
                                                eos_token_id=self.tokenizer.sep_token_id,
                                                pad_token_id=self.tokenizer.pad_token_id,     
                                                repetition_penalty=repetition_penalty,
                                                **model_kwargs)            
            
        captions = []    
        for output in outputs:
            caption = self.tokenizer.decode(output, skip_special_tokens=True)    
            captions.append(caption[len(self.prompt):])
        if return_tag_predict == True:
            if sample:
                return captions, tag_input
            else:
                return captions, tag_input[0:int(len(tag_input)/num_beams)]            
        return captions


def tag2text_caption(pretrained='',**kwargs):
    model = Tag2Text_Caption(**kwargs)
    if pretrained:
        if kwargs['vit'] == 'swin_b':
            model,msg = load_checkpoint_swinbase(model,pretrained,kwargs)
        else:
            model,msg = load_checkpoint(model,pretrained)
        # print('vit:',kwargs['vit'])
        # print('msg_v2',msg)
    return model    


from typing import List
def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
    uninitialized_encoder_weights: List[str] = []
    if decoder.__class__ != encoder.__class__:
        logger.info(
            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
        )

    def tie_encoder_to_decoder_recursively(
        decoder_pointer: nn.Module,
        encoder_pointer: nn.Module,
        module_name: str,
        uninitialized_encoder_weights: List[str],
        skip_key: str,
        depth=0,
    ):
        assert isinstance(decoder_pointer, nn.Module) and isinstance(
            encoder_pointer, nn.Module
        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
            assert hasattr(encoder_pointer, "weight")
            encoder_pointer.weight = decoder_pointer.weight
            if hasattr(decoder_pointer, "bias"):
                assert hasattr(encoder_pointer, "bias")
                encoder_pointer.bias = decoder_pointer.bias                
            # print(module_name+' is tied')    
            return

        encoder_modules = encoder_pointer._modules
        decoder_modules = decoder_pointer._modules
        if len(decoder_modules) > 0:
            assert (
                len(encoder_modules) > 0
            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"

            all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()])
            encoder_layer_pos = 0
            for name, module in decoder_modules.items():
                if name.isdigit():
                    encoder_name = str(int(name) + encoder_layer_pos)
                    decoder_name = name
                    if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(
                        encoder_modules
                    ) != len(decoder_modules):
                        # this can happen if the name corresponds to the position in a list module list of layers
                        # in this case the decoder has added a cross-attention that the encoder does not have
                        # thus skip this step and subtract one layer pos from encoder
                        encoder_layer_pos -= 1
                        continue
                elif name not in encoder_modules:
                    continue
                elif depth > 500:
                    raise ValueError(
                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
                    )
                else:
                    decoder_name = encoder_name = name
                tie_encoder_to_decoder_recursively(
                    decoder_modules[decoder_name],
                    encoder_modules[encoder_name],
                    module_name + "/" + name,
                    uninitialized_encoder_weights,
                    skip_key,
                    depth=depth + 1,
                )
                all_encoder_weights.remove(module_name + "/" + encoder_name)

            uninitialized_encoder_weights += list(all_encoder_weights)

    # tie weights recursively
    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)  


class GroupWiseLinear(nn.Module):
    # could be changed to: 
    # output = torch.einsum('ijk,zjk->ij', x, self.W)
    # or output = torch.einsum('ijk,jk->ij', x, self.W[0])
    def __init__(self, num_class, hidden_dim, bias=True):
        super().__init__()
        self.num_class = num_class
        self.hidden_dim = hidden_dim
        self.bias = bias

        self.W = nn.Parameter(torch.Tensor(1, num_class, hidden_dim))
        if bias:
            self.b = nn.Parameter(torch.Tensor(1, num_class))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.W.size(2))
        for i in range(self.num_class):
            self.W[0][i].data.uniform_(-stdv, stdv)
        if self.bias:
            for i in range(self.num_class):
                self.b[0][i].data.uniform_(-stdv, stdv)

    def forward(self, x):
        # x: B,K,d
        x = (self.W * x).sum(-1)
        if self.bias:
            x = x + self.b
        return x


def init_tokenizer():
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    tokenizer.add_special_tokens({'bos_token':'[DEC]'})
    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       
    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  
    return tokenizer


def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
        
    assert vit in ['base', 'large'], "vit parameter must be base or large"
    if vit=='base':
        vision_width = 768
        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, 
                                           num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
                                           drop_path_rate=0 or drop_path_rate
                                          )   
    elif vit=='large':
        vision_width = 1024
        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, 
                                           num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
                                           drop_path_rate=0.1 or drop_path_rate
                                          )   
    return visual_encoder, vision_width

def is_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https")

def load_checkpoint(model,url_or_filename):
    if is_url(url_or_filename):
        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
        checkpoint = torch.load(cached_file, map_location='cpu') 
    elif os.path.isfile(url_or_filename):        
        checkpoint = torch.load(url_or_filename, map_location='cpu') 
    else:
        raise RuntimeError('checkpoint url or path is invalid')
        
    state_dict = checkpoint['model']
    
    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
                                                                         model.visual_encoder_m)    
    for key in model.state_dict().keys():
        if key in state_dict.keys():
            if state_dict[key].shape!=model.state_dict()[key].shape:
                del state_dict[key]
    
    msg = model.load_state_dict(state_dict,strict=False)
    print('load checkpoint from %s'%url_or_filename)  
    return model,msg
    

def load_checkpoint_swinbase(model,url_or_filename,kwargs):
    if kwargs['image_size'] == 224:
        vision_config_path = 'configs/swin/config_swinB_224.json'
    elif kwargs['image_size'] == 384:
        vision_config_path = 'configs/swin/config_swinB_384.json'
    elif kwargs['image_size'] == 480:
        vision_config_path = 'configs/swin/config_swinB_480.json'
    elif kwargs['image_size'] == 576:
        vision_config_path = 'configs/swin/config_swinB_576.json'
    elif kwargs['image_size'] == 608:
        vision_config_path = 'configs/swin/config_swinB_608.json'
    window_size = read_json(vision_config_path)['window_size']
    # print('--------------')
    # print(url_or_filename)
    # print('--------------')
    if is_url(url_or_filename):
        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
        checkpoint = torch.load(cached_file, map_location='cpu') 
    elif os.path.isfile(url_or_filename):        
        checkpoint = torch.load(url_or_filename, map_location='cpu') 
    else:
        raise RuntimeError('checkpoint url or path is invalid')
        
    state_dict = checkpoint['model']

    for k in list(state_dict.keys()):
        if 'relative_position_bias_table' in k:
            dst_num_pos = (2 * window_size - 1) ** 2
            state_dict[k] = interpolate_relative_pos_embed(state_dict[k], dst_num_pos, param_name=k)
        elif ('relative_position_index' in k) or ('attn_mask' in k):
            del state_dict[k]
    
    msg = model.load_state_dict(state_dict,strict=False)
    print('load checkpoint from %s'%url_or_filename)  
    return model,msg
    




